--- title: "Made-With-ML vs mlem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-iterative-mlem" tools: ["gokumohandas-made-with-ml", "iterative-mlem"] --- # Made-With-ML vs mlem *GraphCanon updated Aug 14, 2026* ## Verdict Pick Made-With-ML if made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows; pick mlem if mLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 26 open issues, last pushed Mar 4, 2026. [mlem](https://mlem.ai) has 718 stars, 42 forks, and 131 open issues, last pushed Sep 13, 2023. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [mlem's repository](https://github.com/iterative/mlem). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [mlem](/tools/iterative-mlem.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | A tool to package, serve, and deploy any ML model on any platform. | | Stars | 49,074 | 718 | | Forks | 7,710 | 42 | | Open issues | 26 | 131 | | Language | Jupyter Notebook | Python | | Adopt for | Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. | MLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools, Inference & Serving, Model Training | Developer Tools, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [mlem](/tools/iterative-mlem.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Archived (8%) | | Days since push | 162d | 1055d | | Archived on GitHub | No | Yes | | Open issues (now) | 26 | 131 | | Stars delta | +371 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/iterative-mlem/trust.md) | ## Shared compatibility - **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [mlem](/tools/iterative-mlem.md) - Python runtime ## Decision facts: Made-With-ML - **Requirements:** A foundational understanding of Python programming is required to fully benefit from the learning resources provided. - **Adopt for:** Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. ## Decision facts: mlem - **Adopt for:** MLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI. ## Choose when ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; mlem is Python. - License: Made-With-ML is MIT, mlem is Apache-2.0. - Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.. - Tags unique to Made-With-ML: data-engineering, data-quality, deep-learning, distributed-ml. - Also covers Model Training. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### Choose mlem if… - mlem is primarily Python; Made-With-ML is Jupyter Notebook. - License: mlem is Apache-2.0, Made-With-ML is MIT. - Tags unique to mlem: cli, deployment, git, model-registry. - Use MLEM if you are looking to deploy ML models quickly using a command-line interface (CLI), making it ideal for teams preferring script-driven integration. ## When NOT to use Made-With-ML - If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. - For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ## When NOT to use mlem - Avoid MLEM if you are working in environments where strict package dependency management is required outside Python, as it might complicate integration with non-Python native services. - If detailed manual configuration of deployment settings is a necessity for your application, consider alternatives that offer more granular control over model serving parameters and configurations. ## Common questions ### What is the difference between Made-With-ML and mlem? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. mlem: A tool to package, serve, and deploy any ML model on any platform.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over mlem? Choose Made-With-ML over mlem when Made-With-ML is primarily Jupyter Notebook; mlem is Python; License: Made-With-ML is MIT, mlem is Apache-2.0; Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Tags unique to Made-With-ML: data-engineering, data-quality, deep-learning, distributed-ml; Also covers Model Training; If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### When should I choose mlem over Made-With-ML? Choose mlem over Made-With-ML when mlem is primarily Python; Made-With-ML is Jupyter Notebook; License: mlem is Apache-2.0, Made-With-ML is MIT; Tags unique to mlem: cli, deployment, git, model-registry; Use MLEM if you are looking to deploy ML models quickly using a command-line interface (CLI), making it ideal for teams preferring script-driven integration. ### When should I avoid Made-With-ML? If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ### When should I avoid mlem? Avoid MLEM if you are working in environments where strict package dependency management is required outside Python, as it might complicate integration with non-Python native services. If detailed manual configuration of deployment settings is a necessity for your application, consider alternatives that offer more granular control over model serving parameters and configurations. ### Is Made-With-ML or mlem more popular on GitHub? Made-With-ML has more GitHub stars (49,074 vs 718). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and mlem open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, mlem: Apache-2.0). ### Where can I find alternatives to Made-With-ML or mlem? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [mlem alternatives](/tools/iterative-mlem/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [mlem markdown twin](/tools/iterative-mlem/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/gokumohandas-made-with-ml-vs-iterative-mlem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Made-With-ML or mlem? Made-With-ML: Slowing. mlem: Archived. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Made-With-ML and mlem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [mlem trust report](/tools/iterative-mlem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_